Analysis of Marker Gene Cut-Off Efficacy
Load necessary libraries:
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FALSE expand, pack, unpackLet’s load the newest marker gene set information, rank them by logFC and put them into a marker gene list.
setwd('./markerGeneEfficacy')
markers_df <- read.csv(file = 'final_subclass_markers.txt', stringsAsFactors = F)
setwd('../')
markers_df$rank <- markers_df$bretigea_ranking_best
markers_df <- markers_df[!duplicated(markers_df$gene), ]
markers_df <- markers_df %>% gather(key, value, subclass)
for (subclass in unique(markers_df$value))
{
list <- markers_df %>% filter(value == subclass)
list <- arrange(list, rank)
list <- list$gene
subclassName <- make.names(subclass)
print(subclassName)
assign(subclassName, list)
if(subclassName == make.names(unique(markers_df$value)[1])){
final_list <- list
}
else{
final_list <- list(final_list, list)
}
}## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"final_list <- lapply(rapply(final_list, enquote, how="unlist"), eval)
names(final_list) <- make.names(unique(markers_df$value))
setwd('./markerGeneEfficacy')
saveRDS(final_list, "broad_markers_list_BRETIGEA.rds")
setwd('../')Now we’ve gotten the markers for each cell type ranked in order of log2FC in the format we need it to run the MGP algorithm.
We’re going to get our cohort data and find the marker genes used across all cohorts for this marker list. So let’s load in our cohort data.
#load in new cohorts QC-ed data, final count matrices and convert them to dataframes with HGNC symbols instead of ENSEMBL ids
cohorts <- c("ROSMAP", "MAYO", "MSBBM10", "MSBBM22", "MSBBM36", "MSBBM44")
for(cohort in cohorts){
if(str_detect(cohort, "MSBB")){
cohort <- gsub('MSB', '', cohort)
}
print(cohort)
matrix_name <- paste0(cohort, "_matrix")
filename <- paste0(matrix_name, ".rds")
setwd('./finalCountMatrices')
matrix <- readRDS(filename)
setwd('../')
assign(matrix_name, matrix)
count_df <- as.data.frame(matrix)
count_df <- tibble:: rownames_to_column(count_df, var="Gene")
setwd('./geneAnno')
gene_anno <- readRDS("gene_anno.rds")
setwd('../')
final_df <- merge(count_df, gene_anno)
new_gene <- final_df$Hgnc_Gene %>% make.names(unique = T) #this is ONE way of dealing with the duplicates, just making them into separate, unique names
final_df$new_gene <- new_gene
n_col <- ncol(count_df)
mgp_df <- final_df[,c(n_col+2,2:n_col)]
count_df <- mgp_df %>% rename(Gene = new_gene)
df_name <- paste0(cohort, "_count_df")
assign(df_name, count_df)
}## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"Let’s read in the marker list we’ll be using and run the MGP QC metrics to find the common marker list (the markers found in all cohorts).
#we define our QC algorithm such that it returns a dataframe with the cell type, markers_used (list of marker genes used ' per cell type), removed_marker_ratios (list of removed marker ratios per cell type) and percent_variance_PC1 (list of variance explained by the first PC per cell type)
mgpQCMetrics <-function(count_df, mgp_markers, remove_minority){
if(!all(c("Gene") %in% colnames(count_df))){
stop("The count_df argument must be a df with a column named Gene (HGNC gene symbols)")
}
mgp_est<- markerGeneProfile::mgpEstimate(exprData=count_df,
genes=mgp_markers,
geneColName="Gene",
outlierSampleRemove=F, # should outlier samples removed. This is done using boxplot stats.
geneTransform =NULL, #function(x){homologene::mouse2human(x)$humanGene}, # this is the default option for geneTransform
groups=NULL, #if there are experimental groups provide them here. if not desired set to NULL
seekConsensus = FALSE, # ensures gene rotations are positive in both of the groups
removeMinority = remove_minority)
i= 0
for(cell in names(mgp_markers)){
i = i + 1
cells_df <- mgp_est$usedMarkerExpression[i] %>% as.data.frame()
masterlist <- paste0(rownames(cells_df), collapse=', ')
num_markers <- length(rownames(cells_df))
rm_marker_ratios <- mgp_est$removedMarkerRatios[i]
if(!is.null(mgp_est$trimmedPCAs[[i]])){
percent_variance <- ((summary(mgp_est$trimmedPCAs[[i]]))[6]) %>% as.data.frame()
percent_variance_PC1 <- percent_variance[2,1]
}
else{
percent_variance_PC1 <- NA
}
if(i==1){
master_df <- data.frame( "markers_used" = masterlist,
"removed_marker_ratios" = rm_marker_ratios,
"percent_variance_PC1" = percent_variance_PC1,
"num_markers" = num_markers)
}
else{
df <- data.frame( "markers_used" = masterlist,
"removed_marker_ratios" = rm_marker_ratios,
"percent_variance_PC1" = percent_variance_PC1,
"num_markers" = num_markers)
master_df <- rbind(master_df, df)
}
}
master_df <- tibble::rownames_to_column(master_df, var = "celltype")
return(master_df)
}
#calculate QC metrics for each marker gene list for each cohort
cohorts <- c("ROSMAP", "MAYO", "MSBBBM10", "MSBBBM22", "MSBBBM36", "MSBBBM44")
setwd('./markerGeneEfficacy')
broad_markers_list_BRETIGEA <- readRDS("broad_markers_list_BRETIGEA.rds")
marker_lists <- c("broad_markers_list_BRETIGEA")
setwd('../')
remove_minority <- FALSE
run_type <- ifelse(remove_minority, "", "_ALL")
for(markers in marker_lists){
for(cohort in cohorts){
if(str_detect(cohort, "MSBB")){
cohort <- gsub('MSBB', '', cohort)
}
print(cohort)
df_name <- paste0(cohort, "_count_df")
mgpResult <- mgpQCMetrics(get(df_name), mgp_markers = get(markers), remove_minority)
mgpResult$cohort <- cohort
mgpName <- paste0("mgpQCResults",cohort, markers, run_type)
assign(mgpName, mgpResult)
setwd('./markerGeneEfficacy')
saveRDS(get(mgpName), paste0(mgpName, ".rds"))
setwd('../')
print(mgpName)
assign(mgpName, mgpResult)
if(cohort =="ROSMAP"){
all_cohorts_QC <- mgpResult
}
else{
all_cohorts_QC <-rbind(all_cohorts_QC, mgpResult)
}
}
all_cohorts_QC$cohort <- factor(all_cohorts_QC$cohort, levels = c("ROSMAP", "BM10", "BM44", "BM22", "BM36", "MAYO"))
all_cohorts_QC <- arrange(all_cohorts_QC, cohort)
setwd('./markerGeneEfficacy')
saveRDS(all_cohorts_QC, paste0("all_cohorts_QC", run_type, ".rds"))
setwd('../')
}## [1] "ROSMAP"
## [1] "mgpQCResultsROSMAPbroad_markers_list_BRETIGEA_ALL"
## [1] "MAYO"
## [1] "mgpQCResultsMAYObroad_markers_list_BRETIGEA_ALL"
## [1] "BM10"
## [1] "mgpQCResultsBM10broad_markers_list_BRETIGEA_ALL"
## [1] "BM22"
## [1] "mgpQCResultsBM22broad_markers_list_BRETIGEA_ALL"
## [1] "BM36"
## [1] "mgpQCResultsBM36broad_markers_list_BRETIGEA_ALL"
## [1] "BM44"
## [1] "mgpQCResultsBM44broad_markers_list_BRETIGEA_ALL"Now that we have QC metrics, which tell us which markers were used to calculate MGPs in each cohort, let’s get the common markers.
FALSE [1] "broad_markers_list_BRETIGEA"Let’s modify the QC metrics function to give us the MGPs too.
#we define our QC algorithm such that it returns a dataframe with the cell type, markers_used (list of marker genes used ' per cell type), removed_marker_ratios (list of removed marker ratios per cell type) and percent_variance_PC1 (list of variance explained by the first PC per cell type)
mgpQCMetricsMod <-function(count_df, mgp_markers, remove_minority){
if(!all(c("Gene") %in% colnames(count_df))){
stop("The count_df argument must be a df with a column named Gene (HGNC gene symbols)")
}
mgp_est<- markerGeneProfile::mgpEstimate(exprData=count_df,
genes=mgp_markers,
geneColName="Gene",
outlierSampleRemove=F, # should outlier samples removed. This is done using boxplot stats.
geneTransform =NULL, #function(x){homologene::mouse2human(x)$humanGene}, # this is the default option for geneTransform
groups=NULL, #if there are experimental groups provide them here. if not desired set to NULL
seekConsensus = FALSE, # ensures gene rotations are positive in both of the groups
removeMinority = remove_minority)
i= 0
for(cell in names(mgp_markers)){
i = i + 1
cells_df <- mgp_est$usedMarkerExpression[i] %>% as.data.frame()
masterlist <- paste0(rownames(cells_df), collapse=', ')
num_markers <- length(rownames(cells_df))
rm_marker_ratios <- mgp_est$removedMarkerRatios[i]
if(!is.null(mgp_est$trimmedPCAs[[i]])){
percent_variance <- ((summary(mgp_est$trimmedPCAs[[i]]))[6]) %>% as.data.frame()
percent_variance_PC1 <- percent_variance[2,1]
}
else{
percent_variance_PC1 <- NA
}
if(i==1){
master_df <- data.frame( "markers_used" = masterlist,
"removed_marker_ratios" = rm_marker_ratios,
"percent_variance_PC1" = percent_variance_PC1,
"num_markers" = num_markers)
}
else{
df <- data.frame( "markers_used" = masterlist,
"removed_marker_ratios" = rm_marker_ratios,
"percent_variance_PC1" = percent_variance_PC1,
"num_markers" = num_markers)
master_df <- rbind(master_df, df)
}
}
master_df <- tibble::rownames_to_column(master_df, var = "celltype")
return(list(master_df, mgp_est))
}Time to run the MGP QC metrics (modified function) on loop with mgp calc as well.
setwd('./markerGeneEfficacy')
#broad_markers_list_BRETIGEA_common_final <- readRDS("broad_markers_list_BRETIGEA_common_final.rds")
broad_markers_list_BRETIGEA_ALL_common_final <-
readRDS("broad_markers_list_BRETIGEA_ALL_common_final.rds")
setwd('../')
marker_lists <- ("broad_markers_list_BRETIGEA_ALL_common_final")
cohorts <- c("ROSMAP", "MAYO", "MSBBBM10", "MSBBBM22", "MSBBBM36", "MSBBBM44")
remove_minority <- FALSE
run_type <- ifelse(remove_minority, "", "_ALL")
for(markers in marker_lists){
for(cohort in cohorts){
if(str_detect(cohort, "MSBB")){
cohort <- gsub('MSBB', '', cohort)
}
print(cohort)
df_name <- paste0(cohort, "_count_df")
if(cohort == "ROSMAP"){
covars <- c("sex","age_death")
}
else{
covars <- c("msex","AgeAtDeath")
}
setwd('./QCpipelineResults')
v_name <- paste0("v_", cohort)
voom <- readRDS(paste0(v_name, ".rds"))
assign(v_name, voom)
setwd('../')
pheno_df <- voom$targets
if(cohort == "ROSMAP" || cohort == "MAYO"){
pheno_df<- pheno_df %>%
rename(
projid = SampleID,
)
}
else{
pheno_df<- pheno_df %>%
rename(
projid = sampleIdentifier,
)
}
for(increment in seq(from=5, to=50, by=5)){
mgp_markers <- lapply(get(markers), `[`, 1:increment)
mgp <- mgpQCMetricsMod(get(df_name), mgp_markers = mgp_markers, remove_minority)
estimates <- mgp[[2]]$estimates
for(estimate in names(estimates)){
print(estimate)
mgp_est_cell <- as.data.frame(estimates[estimate])
names(mgp_est_cell) <- estimate
if(estimate == names(estimates)[1]){
mgp_est_df <- mgp_est_cell
}
else{
mgp_est_df <- cbind(mgp_est_df, mgp_est_cell)
}
}
mgp_est_df <- rownames_to_column(mgp_est_df, var = "subjectID")
mgp_est_df$cohort <- cohort
mgp_est_df$increment <- increment
mgp_result <- mgp[[1]]
mgp_result$cohort <- cohort
mgp_result$increment <- increment
mgp_df <- mgp[[2]]
mgp_df <- mgp_df$estimates %>% as.data.frame()
colnames(mgp_df) <- names(get(markers))
mgp_df <- mgp_df %>% tibble::rownames_to_column(var = 'projid')
# merge mgp data frame with sample metadata data frame
mgp_df = merge(pheno_df, mgp_df, by = 'projid')
for(cell in names(get(markers))){
mgp_df[,cell] <- as.numeric(scale(mgp_df[,cell], center = TRUE, scale = TRUE))
}
mgp_df$increment <- increment
mgp_name <- paste0("mgp_",cohort, run_type, "_", increment)
mgp_ZScored_name <- paste0(mgp_name, "_ZScored")
assign(mgp_ZScored_name, mgp_df)
setwd('./markerGeneEfficacy')
saveRDS(mgp_df, paste0(mgp_ZScored_name, ".rds"))
setwd('../')
if(increment == 5){
mgp_percent_variance <- data.frame("celltype" = mgp_result$celltype,
"percent_variance" = mgp_result$percent_variance_PC1,
"cohort" = mgp_result$cohort,
"marker_cap" = mgp_result$increment,
"markers_used" = mgp_result$num_markers)
final_est_df <- mgp_est_df
}
else{
curr_percent_variance <- data.frame("celltype" = mgp_result$celltype,
"percent_variance" = mgp_result$percent_variance_PC1,
"cohort" = mgp_result$cohort,
"marker_cap" = mgp_result$increment,
"markers_used" = mgp_result$num_markers)
mgp_percent_variance <-rbind(mgp_percent_variance, curr_percent_variance)
final_est_df <- rbind(final_est_df, mgp_est_df)
}
}
name <- paste0("mgpPercentVar",cohort, markers,run_type)
print(name)
assign(name, mgp_percent_variance)
setwd('./markerGeneEfficacy')
saveRDS(get(name), paste0(name, ".rds"))
setwd('../')
if(cohort =="ROSMAP"){
all_cohort_PV <- mgp_percent_variance
all_mgp_est <- final_est_df
}
else{
all_cohort_PV <-rbind(all_cohort_PV, mgp_percent_variance)
all_mgp_est <- rbind(all_mgp_est, final_est_df)
}
}
all_cohort_PV$cohort <- factor(all_cohort_PV$cohort, levels = c("ROSMAP", "BM10", "BM44", "BM22", "BM36", "MAYO"))
all_cohort_PV <- arrange(all_cohort_PV, cohort)
all_mgp_est$cohort <- factor(all_mgp_est$cohort, levels = c("ROSMAP", "BM10", "BM44", "BM22", "BM36", "MAYO"))
all_mgp_est <- arrange(all_mgp_est, cohort)
setwd('./markerGeneEfficacy')
saveRDS(all_cohort_PV, paste0("all_cohort_PV", run_type, ".rds"))
saveRDS(all_mgp_est, paste0("all_mgp_est", run_type, ".rds"))
setwd('../')
}## [1] "ROSMAP"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "mgpPercentVarROSMAPbroad_markers_list_BRETIGEA_ALL_common_final_ALL"
## [1] "MAYO"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "mgpPercentVarMAYObroad_markers_list_BRETIGEA_ALL_common_final_ALL"
## [1] "BM10"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "mgpPercentVarBM10broad_markers_list_BRETIGEA_ALL_common_final_ALL"
## [1] "BM22"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "mgpPercentVarBM22broad_markers_list_BRETIGEA_ALL_common_final_ALL"
## [1] "BM36"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "mgpPercentVarBM36broad_markers_list_BRETIGEA_ALL_common_final_ALL"
## [1] "BM44"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "mgpPercentVarBM44broad_markers_list_BRETIGEA_ALL_common_final_ALL"Let’s plot these things because they’re monstrosities and I have no idea what the trend looks like.
remove_minority <- FALSE
run_type <- ifelse(remove_minority, "", "_ALL")
setwd('./markerGeneEfficacy')
all_mgp_est<- readRDS(paste0("all_mgp_est", run_type, ".rds"))
setwd('../')
cell_types = names(broad_markers_list_BRETIGEA_ALL_common_final)
marker_efficacy_by_mgps <- lapply(cell_types,function(celltype) {
print(celltype)
mgp_plot <-
ggplot(all_mgp_est, aes(x=increment, y=get(celltype), color=cohort))+
geom_point()+
facet_grid(~cohort, scale = 'free_x', space = 'free_x') +
labs(title= paste0(celltype, " Markers Included Vs. MGPs"),x="Top Markers Used", y = "MGP estimate")+
theme_minimal()
return(mgp_plot)
})## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"n <- length(marker_efficacy_by_mgps)
nCol <- floor(sqrt(n))
setwd('./markerGeneEfficacy')
pdf(file = paste0("graphed_mgps", run_type, ".pdf"),
width = 25, # The width of the plot in inches
height = 30)
print(do.call("grid.arrange", c(marker_efficacy_by_mgps, ncol=nCol)))## TableGrob (6 x 4) "arrange": 21 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]
## 4 4 (1-1,4-4) arrange gtable[layout]
## 5 5 (2-2,1-1) arrange gtable[layout]
## 6 6 (2-2,2-2) arrange gtable[layout]
## 7 7 (2-2,3-3) arrange gtable[layout]
## 8 8 (2-2,4-4) arrange gtable[layout]
## 9 9 (3-3,1-1) arrange gtable[layout]
## 10 10 (3-3,2-2) arrange gtable[layout]
## 11 11 (3-3,3-3) arrange gtable[layout]
## 12 12 (3-3,4-4) arrange gtable[layout]
## 13 13 (4-4,1-1) arrange gtable[layout]
## 14 14 (4-4,2-2) arrange gtable[layout]
## 15 15 (4-4,3-3) arrange gtable[layout]
## 16 16 (4-4,4-4) arrange gtable[layout]
## 17 17 (5-5,1-1) arrange gtable[layout]
## 18 18 (5-5,2-2) arrange gtable[layout]
## 19 19 (5-5,3-3) arrange gtable[layout]
## 20 20 (5-5,4-4) arrange gtable[layout]
## 21 21 (6-6,1-1) arrange gtable[layout]dev.off()## png
## 2setwd('../')setwd('./markerGeneEfficacy')
all_cohort_PV <- readRDS(paste0("all_cohort_PV", run_type, ".rds"))
setwd('../')
marker_efficacy_plot <-
ggplot(all_cohort_PV,
aes(x=markers_used, y=percent_variance,
group = celltype, color=celltype))+
geom_line() +
geom_point()+
geom_hline(yintercept = 0.35) +
facet_grid(~cohort, scale = 'free_x', space = 'free_x') +
labs(title="Markers Included Vs. Percent Variance Explained",x="Top Markers Used", y = "Percent Variance Explained by PC 1")+
theme_minimal()
setwd('./markerGeneEfficacy')
saveRDS(marker_efficacy_plot, paste0("marker_efficacy_plot", run_type, ".rds"))
pdf(file = paste0("marker_efficacy", run_type, ".pdf"),
width = 21, # The width of the plot in inches
height = 14)
print(marker_efficacy_plot)
dev.off()## png
## 2print(marker_efficacy_plot)
setwd('../')Let’s now look at our Z-scored mgps and run mega-analysis. First things first we have to organize the Z scored mgps by the increments we ran them at and group them into dfs.
setwd('./markerGeneEfficacy')
#broad_markers_list_BRETIGEA_common_final <- readRDS("broad_markers_list_BRETIGEA_common_final.rds")
broad_markers_list_BRETIGEA_ALL_common_final <-
readRDS("broad_markers_list_BRETIGEA_ALL_common_final.rds")
setwd('../')
marker_lists <- ("broad_markers_list_BRETIGEA_ALL_common_final")
cohorts <- c("ROSMAP", "MAYO", "BM10", "BM22", "BM36", "BM44")
setwd('./rawCohortData')
BMidsAcross <- readRDS("allMSBBIDs.rds")
setwd('../')
BMidsAcross <- BMidsAcross %>%
rename(
projid = sampleIdentifier
)
for(markers in marker_lists){
for(increment in seq(from=5, to=50, by=5)){
for(cohort in cohorts){
print(cohort)
mgp_name <- paste0("mgp_",cohort, run_type, "_", increment)
setwd('./markerGeneEfficacy')
mgp_ZScored_name <- paste0(mgp_name, "_ZScored")
mgp_Z_df <- readRDS(paste0(mgp_ZScored_name, ".rds"))
assign(mgp_ZScored_name, mgp_Z_df )
setwd('../')
cell_types <- names(get(markers))
if(cohort == "ROSMAP"){
mgp_Z_df <- mgp_Z_df %>%
rename(
AgeAtDeath = age_death
)
}
mgp_Z_df <- mgp_Z_df %>% select(cell_types, "projid", "msex", "LOAD", "AgeAtDeath")
mgp_Z_df$cohort <- cohort
if(cohort == "ROSMAP"){
mega_mgp <- mgp_Z_df
}
else{
mega_mgp <- rbind(mega_mgp, mgp_Z_df)
}
}
#getting overlapping identifiers for BM cohorts
BMidsAcross <- BMidsAcross[!duplicated(BMidsAcross),]
MGPsBM <- mega_mgp %>% filter(str_detect(cohort, "BM"))
allBMs <- merge(MGPsBM, BMidsAcross)
allBMs <- allBMs[,-1]
allBMs <- allBMs%>%select(individualIdentifier,everything())
allBMs <- allBMs %>%
rename(
projid = individualIdentifier
)
mega_mgp <- mega_mgp %>% filter(!str_detect(cohort, "BM"))
mega_mgp <- rbind(mega_mgp, allBMs)
#save mega_mgp
setwd('./markerGeneEfficacy')
saveRDS(mega_mgp, paste0("megaMGP_", markers, "_", increment, ".rds"))
assign(paste0("megaMGP_", markers, "_", increment), mega_mgp)
setwd('../')
}
}## [1] "ROSMAP"## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(cell_types)` instead of `cell_types` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"
## [1] "ROSMAP"
## [1] "MAYO"
## [1] "BM10"
## [1] "BM22"
## [1] "BM36"
## [1] "BM44"Okay, time to calculate the associations between LOAD and the mgps for each increment.
setwd('./markerGeneEfficacy')
#broad_markers_list_BRETIGEA_common_final <- readRDS("broad_markers_list_BRETIGEA_common_final.rds")
broad_markers_list_BRETIGEA_ALL_common_final <-
readRDS("broad_markers_list_BRETIGEA_ALL_common_final.rds")
setwd('../')
marker_lists <- ("broad_markers_list_BRETIGEA_ALL_common_final")
cohorts <- c("ROSMAP", "MAYO", "BM10", "BM22", "BM36", "BM44")
remove_minority <- FALSE
run_type <- ifelse(remove_minority, "", "_ALL")
for(markers in marker_lists){
for(increment in seq(from=5, to=50, by=5)){
print(markers)
print(increment)
setwd('./markerGeneEfficacy')
mega_mgp <- readRDS(paste0("megaMGP_", markers, "_", increment, ".rds"))
assign(paste0("megaMGP_", markers, run_type, "_", increment), mega_mgp)
setwd('../')
covars <- c("msex", "AgeAtDeath")
colnames(mega_mgp) <- make.names(colnames(mega_mgp))
cell_types <- make.names(names(get(markers)))
pathology <- ("LOAD")
model.data <- mega_mgp
LOAD_results <- sapply(cell_types,function(celltype) {
sapply(pathology, function(pathology) {
print(celltype)
form <- as.formula(paste0(celltype,"~",pathology," + ", "(1 | projid )" ,
" + ", "cohort" , " + ", paste0(covars,collapse=" + ")))
model <- lmer(data=model.data,form)
return(model)
})
})
results <- sapply(cell_types,function(celltype) {
print(celltype)
form <- as.formula(paste0(celltype,"~" ," + ", "(1 | projid )" ," + ", "cohort" ,
" +", paste0(covars,collapse=" + ")))
model2 <- lmer(data=model.data,form)
return(model2)
})
for(cell in cell_types){
mod1Name <- paste0(cell, ".LOAD")
mod2Name <- cell
print(mod1Name)
print(mod2Name)
significance <- (anova(LOAD_results[mod1Name][[1]], results[mod2Name][[1]]))$`Pr(>Chisq)`[2]
confInt <- confint(LOAD_results[mod1Name][[1]],level = 0.95, oldNames=FALSE)
upperBound <- confInt[4,2]
lowerBound <- confInt[4,1]
if(cell == cell_types[1]){
celltype_sig <- data.frame("celltype"=cell, significance, "beta" = coef(summary(LOAD_results[mod1Name][[1]]))[2,1], "std.err" = coef(summary(LOAD_results[mod1Name][[1]]))[2,2] ,
"lowerBound" = lowerBound, "upperBound" = upperBound)
}
else{
temp <- data.frame("celltype"=cell, significance, "beta" = coef(summary(LOAD_results[mod1Name][[1]]))[2,1], "std.err" = coef(summary(LOAD_results[mod1Name][[1]]))[2,2],
"lowerBound" = lowerBound, "upperBound" = upperBound)
celltype_sig <- rbind(celltype_sig, temp)
}
}
celltype_sig$fdr <- p.adjust(celltype_sig$significance, method="fdr")
celltype_sig$bonf <- p.adjust(celltype_sig$significance, method="bonferroni")
celltype_sig$SIG <- celltype_sig$fdr <0.05
celltype_sig$SIGBONF <- celltype_sig$bonf <0.05
setwd('./markerGeneEfficacy')
saveRDS(celltype_sig, paste0("mega_results_", markers, run_type, "_", increment, ".rds"))
assign(paste0("mega_results_", markers, run_type, "_", increment), celltype_sig)
setwd('../')
}
}## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 5
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"## boundary (singular) fit: see ?isSingular## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"## boundary (singular) fit: see ?isSingular## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"## boundary (singular) fit: see ?isSingular## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"## boundary (singular) fit: see ?isSingular## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## Warning in zeta(shiftpar, start = opt[seqpar1][-w]): slightly lower deviances
## (diff=-4.54747e-13) detected## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep## Warning in zetafun(np, ns): slightly lower deviances (diff=-4.54747e-13)
## detected## Warning in FUN(X[[i]], ...): non-monotonic profile for sd_(Intercept)|projid## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## sd_(Intercept)|projid: falling back to linear interpolation## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep## Warning in FUN(X[[i]], ...): non-monotonic profile for sd_(Intercept)|projid## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## sd_(Intercept)|projid: falling back to linear interpolation## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 10
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 15
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"## boundary (singular) fit: see ?isSingular## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"## boundary (singular) fit: see ?isSingular## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep## Warning in FUN(X[[i]], ...): non-monotonic profile for sd_(Intercept)|projid## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## sd_(Intercept)|projid: falling back to linear interpolation## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 20
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 25
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 30
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 35
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 40
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 45
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "broad_markers_list_BRETIGEA_ALL_common_final"
## [1] 50
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte"
## [1] "Endothelial"
## [1] "L2.3.IT"
## [1] "L3.5.IT"
## [1] "L4.IT"
## [1] "L5.ET"
## [1] "L5.6.IT.Car3"
## [1] "L5.6.NP"
## [1] "L6.CT"
## [1] "L6.IT"
## [1] "L6b"
## [1] "LAMP5"
## [1] "Microglia"
## [1] "Oligodendrocyte"
## [1] "OPC"
## [1] "PAX6"
## [1] "Pericyte"
## [1] "PVALB"
## [1] "SST"
## [1] "VIP"
## [1] "VLMC"
## [1] "Astrocyte.LOAD"
## [1] "Astrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Endothelial.LOAD"
## [1] "Endothelial"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L2.3.IT.LOAD"
## [1] "L2.3.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L3.5.IT.LOAD"
## [1] "L3.5.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L4.IT.LOAD"
## [1] "L4.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.ET.LOAD"
## [1] "L5.ET"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.IT.Car3.LOAD"
## [1] "L5.6.IT.Car3"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L5.6.NP.LOAD"
## [1] "L5.6.NP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.CT.LOAD"
## [1] "L6.CT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6.IT.LOAD"
## [1] "L6.IT"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "L6b.LOAD"
## [1] "L6b"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "LAMP5.LOAD"
## [1] "LAMP5"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Microglia.LOAD"
## [1] "Microglia"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Oligodendrocyte.LOAD"
## [1] "Oligodendrocyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "OPC.LOAD"
## [1] "OPC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PAX6.LOAD"
## [1] "PAX6"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "Pericyte.LOAD"
## [1] "Pericyte"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "PVALB.LOAD"
## [1] "PVALB"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "SST.LOAD"
## [1] "SST"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VIP.LOAD"
## [1] "VIP"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...## [1] "VLMC.LOAD"
## [1] "VLMC"## refitting model(s) with ML (instead of REML)
## Computing profile confidence intervals ...Alright, let’s plot.
for(markers in marker_lists){
for(increment in seq(from=5, to=50, by=5)){
print(markers)
setwd('./markerGeneEfficacy')
mega_mgp_results <- readRDS(paste0("mega_results_", markers, run_type, "_", increment, ".rds"))
mega_mgp_results$increment <- increment
if(increment == 5){
full_mega_results <- mega_mgp_results
}
else{
full_mega_results <-rbind(full_mega_results, mega_mgp_results)
}
assign(paste0("mega_mgp_results_", markers, run_type, "_", increment), mega_mgp_results)
setwd('../')
all_beta_mega = mega_mgp_results
all_beta_mega$ub = all_beta_mega$beta + all_beta_mega$std.err
all_beta_mega$lb = all_beta_mega$beta - all_beta_mega$std.err
#add the *** label for significant vs. not significant
annotation_label_mega <- all_beta_mega
annotation_label_mega$mark <- ifelse(annotation_label_mega$bonf <0.05,"***", "")
mega_analysis_plot = all_beta_mega %>%
ggplot(aes(x = celltype, y = beta)) +
geom_hline(yintercept = 0) +
geom_bar(stat = "identity", show.legend = FALSE) +
scale_fill_manual() +
geom_errorbar(aes(ymin = lb, ymax = ub), width = .33) +
ylab('LOAD (Beta)') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
ggtitle(paste0("Mega Analysis Results for ", increment, " Markers Marker List")) +
geom_text(x = annotation_label_mega$celltype, y = 0.3,
label = annotation_label_mega$mark,
colour = "black", size=6)
print(mega_analysis_plot)
setwd('./markerGeneEfficacy')
ggsave(paste0("mega_analysis_", markers, "_", increment, "_plot", ".png"))
setwd('../')
}
}## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
## [1] "broad_markers_list_BRETIGEA_ALL_common_final"## Saving 7 x 5 in image
full_mega_analysis_plot = full_mega_results %>%
ggplot(aes(x=increment, y=beta, group=celltype)) +
geom_line(aes(color=celltype))+
geom_point(aes(color=celltype)) +
ggtitle(paste0("Mega Analysis Results by Markers Included"))
print(full_mega_analysis_plot)
setwd('./markerGeneEfficacy')
ggsave(paste0("mega_analysis_", markers, "_full_plot", ".png"))## Saving 7 x 5 in imagesetwd('../')Let’s look at the marker gene associations with LOAD.
frenchFryPlot<-function(AD_coef, AD_pval, marker_list, cohort){
pathology_df <- AD_pval
marker_df <- data.frame(unlist(marker_list, use.names=F),rep(names(marker_list),
lengths(marker_list)))
colnames(marker_df) <- c("marker", "celltype")
for (marker in names(marker_list)){
df <- pathology_df %>% filter(marker == marker)
if (marker == names(marker_list)[1]){
result <- df
}
else{
result <- rbind(result, df)
}
}
final_result <- merge(result, AD_coef, by="gene")
final_result$signedP <- -log10(final_result$pval) *(sign(final_result$coef))
merge_markers<- marker_df %>% rename( gene = marker)
final_result <- merge(merge_markers, final_result)
final_result$cohort <- c(cohort)
return(unique(final_result))
}
setwd('./markerGeneEfficacy')
broad_markers_list_BRETIGEA_ALL_common_final <- readRDS("broad_markers_list_BRETIGEA_ALL_common_final.rds")
setwd('../')
marker_lists <- ("broad_markers_list_BRETIGEA_ALL_common_final")
cohorts <- c("ROSMAP", "MAYO", "BM10", "BM22", "BM36", "BM44")
setwd('./geneAnno')
gene_anno <- readRDS("gene_anno.rds")
setwd('../')
for (markers in marker_lists){
for(cohort in cohorts){
markers_for_plot <- get(markers)
print(cohort)
lmod_name <- paste0("lmod_" ,cohort)
filename <- paste0(lmod_name, ".rds")
setwd('./cohortQCMods')
lmod <- readRDS(filename)
setwd('../')
assign(lmod_name, lmod)
eb <- eBayes(lmod,robust = T)
if(cohort == "ROSMAP"){
n=11
}
if(cohort == "MAYO"){
n=24
}
if(cohort == "BM10"){
n=18
}
if(cohort == "BM22"){
n=21
}
else if(cohort == "BM36" || cohort == "BM44"){
n=19
}
print(n)
gene_v_AD <-lmod$coefficients[,c(n)]
gene_v_AD <- as.data.frame(gene_v_AD)
gene_v_AD <- tibble:: rownames_to_column(gene_v_AD, var="Gene")
final_df <- merge(gene_v_AD, gene_anno)
final_df$new_gene<- final_df$Hgnc_Gene %>% make.names(unique = T) #this is ONE way of dealing with the duplicates, just making them into separate, unique names
#add new_gene column w/ Hgnc_Gene values filtered to have no duplicates
AD_coef <- final_df[,c(2,4)]
colnames(AD_coef) <- c("coef", "gene")
coef_df <- AD_coef #dataframe with coefficient column and HGNC gene name column
#get p values
p_geneAD <- eb$p.value
p_geneAD <-p_geneAD[,c(n)]
p_geneAD <- as.data.frame(p_geneAD)
p_geneAD <- tibble:: rownames_to_column(p_geneAD, var="Gene")
final_df <- merge(p_geneAD, gene_anno)
new_gene <- final_df$Hgnc_Gene %>% make.names(unique = T) #this is ONE way of dealing with the duplicates, just making them into separate, unique names
#add new_gene column w/ Hgnc_Gene values filtered to have no duplicates
final_df$new_gene <- new_gene
AD_pval <- final_df[,c(2,4)]
colnames(AD_pval) <- c("pval", "gene")
gene_pathology_association <- frenchFryPlot(AD_coef, AD_pval, cohort, marker_list =markers_for_plot)
assign(paste0("fry_plot", cohort), gene_pathology_association)
}
fry_df <- rbind(fry_plotROSMAP, fry_plotMAYO, fry_plotBM10,
fry_plotBM22, fry_plotBM36, fry_plotBM44)
fry_df$fdr <- p.adjust(fry_df$pval, method="fdr")
fry_df$signedFDR <- -log10(fry_df$fdr) *(sign(fry_df$signedP))
fry_df$sig <- ifelse(fry_df$pval < 0.05, "#94C973", "#808080")
for (cell in names((markers_for_plot))){
celltype_fry_df <- fry_df %>% filter(celltype == cell)
celltype_heat_map <- ggplot(celltype_fry_df, aes(cohort, gene, fill= signedP))+
theme_minimal() + geom_tile() +
scale_fill_gradient2(low="darkblue", high="darkgreen", guide="colorbar") +
ggtitle(paste0("Significance of \n" , cell, " ", markers ,
"\n marker genes per cohort"))
celltype_french <- ggplot(celltype_fry_df, aes(x= gene,y= signedP))+
theme_minimal() + theme(axis.text.x = element_text(angle = 45)) +
geom_bar(stat="identity", fill = celltype_fry_df$sig)+
facet_wrap(~cohort, scales = 'free_x',nrow=6) +
ggtitle(paste0("Significance of \n" , cell, " ", markers ,
"\n marker genes per cohort"))
celltype_heat_map
celltype_french
setwd('./frenchHeat')
heat_name <- paste0("heat_map",make.names(cell))
assign(heat_name, celltype_heat_map)
print(get(heat_name))
saveRDS(get(heat_name), paste0(heat_name, ".rds"))
ggsave(paste0(heat_name, markers, "_plot", ".png"), width = 15, height = 12)
french_name <- paste0("french_fry",make.names(cell))
assign(french_name, celltype_french)
print(get(french_name))
saveRDS(get(french_name), paste0(french_name, ".rds"))
ggsave(paste0(french_name, markers, "_plot", ".png"), width = 15, height = 20)
setwd('../')
}
}## [1] "ROSMAP"
## [1] 11
## [1] "MAYO"
## [1] 24
## [1] "BM10"
## [1] 18
## [1] "BM22"
## [1] 21
## [1] "BM36"
## [1] 19
## [1] "BM44"
## [1] 19
